COMMERCE FAKE PRODUCT REVIEWS MONITORING AND DETECTION
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The document outlines a novel supervised probabilistic method for detecting fake reviews on e-commerce platforms, leveraging the differences in distributions between genuine and fraudulent reviews. It highlights the importance of using various features, such as linguistic and behavioral attributes, and describes a comprehensive mixed probability approach for accurate classification. The proposed system addresses common drawbacks in existing methods, including data reliance and adaptability to evolving fraudulent strategies, while providing a scalable solution for maintaining review integrity.
COMMERCE FAKE PRODUCT REVIEWS MONITORING AND DETECTION
- 1. COMMERCE FAKE PRODUCT REVIEWS MONITORING AND DETECTION SYSTEM Abstract Online consumer reviews play an important role in helping consumers judge the quality and authenticity of products on e-commerce platforms. However, the constant presence of fake reviews on these platforms has significantly impacted the operation and development of e- commerce platforms. In this study, we develop a novel supervised probabilistic method to detect fake reviews by utilizing the difference in the distribution of non-fraudulent reviews and that of fake reviews. Specifically, we first derive the univariate distributions of several unique features (linguistic, behavioral, and interrelationship features). We then integrate these distributions into two mixed distributions according to their labels to represent the overall difference between non-fraudulent reviews and fake reviews. Next, we randomly generate synthetic review data points with different labels from the above mixed distributions. Finally, we train a Multilayer Perceptron model by using these synthetic review data to obtain a classifier. We conducted several experiments to test the model using several original real-world review datasets. Numerical results indicated that the proposed supervised method outperformed some well-known sampling models and fake review detection methods, in terms of classification accuracy. Moreover, we extend the proposed method to handle the scenarios with small samples of raw review data. This study contributes to the literature by exploiting the difference in the distribution of non-fraudulent reviews and that of fraudulent reviews, which can improve the accuracy of fake review detection for online platforms. Existing System Detecting fake reviews on e-commerce platforms is critical for maintaining credibility and trust among users. A supervised general mixed probability approach offers a robust method to sift through reviews and identify potential fraudulent ones. By leveraging a combination of machine learning algorithms and probabilistic models, this approach aims to analyze various features within reviews to distinguish between genuine and fake content.The system employs a supervised learning framework, utilizing a labeled dataset to train the model. Features such as sentiment analysis, linguistic patterns, reviewer behavior, review timing, and product information are considered to create a comprehensive feature set. These features are then processed through a mixed probability model, which combines the strengths of different
- 2. probabilistic techniques, such as Bayesian methods or Hidden Markov Models, to assess the likelihood of a review being authentic or deceptive.By employing a mixed probability approach, this system can effectively handle diverse types of fake reviews, adapting to evolving strategies used by malicious actors. Additionally, continuous model retraining and adaptation ensure its ability to stay updated with new trends in fraudulent review practices.The goal of this approach is not only to accurately detect fake reviews but also to provide e-commerce platforms with a scalable and adaptable solution to maintain the integrity of their review systems, fostering a trustworthy environment for both consumers and businesses. Drawback in Existing System Data Dependence: This approach heavily relies on labeled datasets for training. Obtaining and maintaining a large and diverse labeled dataset can be challenging and costly. Moreover, the model's effectiveness might decrease if the dataset doesn’t adequately represent evolving fraudulent review tactics. Feature Engineering Complexity: Extracting relevant features from reviews requires sophisticated natural language processing (NLP) techniques. Designing and engineering these features can be complex and computationally intensive. Additionally, the model's performance heavily relies on the quality and relevance of these features. Adaptability to New Techniques: Fraudulent review strategies evolve over time, and new methods constantly emerge. The model might struggle to adapt quickly to these changes, requiring frequent updates and retraining to maintain its effectiveness. Resource Intensive: Implementing and maintaining a mixed probability approach can be computationally demanding. This might pose challenges for smaller e-commerce platforms with limited resources. Proposed System Data Collection: Description of the dataset acquisition process, emphasizing the need for a diverse and labeled dataset. Preprocessing and Feature Engineering: Details on data preprocessing techniques and the selection of various features (linguistic, behavioral, temporal) for model training.
- 3. Supervised Learning Framework: Explanation of the mixed probability approach involving Bayesian classifiers, Hidden Markov Models, or ensemble methods. Model Training and Evaluation: Methodology for model training, validation, and performance evaluation using appropriate metrics. Algorithm Sentiment Analysis: Using algorithms like VADER (Valence Aware Dictionary and sEntiment Reasoner) or supervised machine learning models to determine sentiment polarity. NLP Techniques: Leveraging techniques like word embeddings (Word2Vec, GloVe) or language models (BERT, GPT) for semantic understanding. Linguistic Features: Analyzing word frequency, syntactic patterns, or grammar structures. Advantages Incorporates Various Features: Leverages linguistic, temporal, and behavioral attributes within reviews, offering a comprehensive assessment for identifying fraudulent patterns. Comprehensive Feature Set: Utilizes diverse features such as sentiment analysis, linguistic patterns, reviewer behavior, and temporal information, improving the accuracy of detecting fake reviews. Mixed Probability Models: Combines different probabilistic techniques, allowing the system to adapt to emerging fraudulent review strategies over time. Robust Classification: Considers multiple dimensions, minimizing misclassification of genuine reviews as fake, thus reducing false alarms. Software Specification Processor : I3 core processor Ram : 4 GB Hard disk : 500 GB
- 4. Software Specification Operating System : Windows 10 /11 Frond End : JAVA Swing Back End : Mysql Server IDE Tools : Eclipse Browser : Microsoft Edge